IEEE INFOCOM 2023

Program at a Glance

IEEE INFOCOM 2023

Session Break-1-Day3: Coffee Break Session Lunch-Day3: Conference Lunch Session Break-2-Day3: Coffee Break

Session A-7: ML Security Session A-8: Internet/Web Security Session A-9: IoT Session A-10: Distributed Learning

Session B-7: PHY Networking Session B-8: Scheduling Session B-9: Wireless Systems Session B-10: Learning

Session C-7: Wireless Charging Session C-8: Network Applications Session C-9: Crowdsourcing Session C-10: Security and Trust

Session D-7: Edge Computing 1 Session D-8: Edge Computing 2 Session D-9: Cross-technology Communications Session D-10: Edge Computing 3

Session E-7: ML Applications Session E-8: Video and Web Applications Session E-9: SDN Session E-10: Video Streaming 4

Session F-7: Distributed Learning Session F-8: Network Design and Fault Tolerance Session F-9: Memory/Cache Management 2 Session F-10: Federated Learning 6

Session G-7: TCP and Congestion Control Session G-9: Cloud/Edge Computing 2 Session G-10: Miscellaneous

Session Poster-2: Poster Session 2

Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

Wangqiu Zhou, Hao Zhou, Xiang Cui and Xinyu Wang (University of Science and Technology of China, China); Xiaoyan Wang (Ibaraki University, Japan); Zhi Liu (The University of Electro-Communications, Japan)

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn't be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%.

Speaker Hao Zhou (University of Science and Technology of China)

Hao Zhou (Member, IEEE) received the BS and PhD degrees in computer science from the University of Science and Technology of China, Hefei, China, in 1997 and 2002, respectively. From 2014 to 2016, he was a project lecturer with the National Institute of Informatics (NII), Japan, and currently he is an associate professor with the University of Science and Technology of China, Hefei, China. His research interests include Internet of Things, wireless communication, and software engineering.

Concurrent Charging with Wave Interference

Yuzhuo Ma, Dié Wu and Meixuan Ren (Sichuan Normal University, China); Jian Peng (Sichuan University, China); Jilin Yang and Tang Liu (Sichuan Normal University, China)

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.

Speaker Mazhuo Yu (Sichuan Normal University)

Yuzhuo Ma received the BS degree in mechanical engineering from Soochow University, Suzhou, China, in 2019. She is studying towards the MS degree in the College of Computer Science, Sichuan Normal University. Her research interests focus on wireless charging and wireless sensor networks.

Utilizing the Neglected Back Lobe for Mobile Charging

Meixuan Ren, Dié Wu and Jing Xue (Sichuan Normal University, China); Wenzheng Xu and Jian Peng (Sichuan University, China); Tang Liu (Sichuan Normal University, China)

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.

Speaker Tang Liu (Sichuan Normal University)

Tang Liu is currently a Professor and vice dean of College of Computer Science at Sichuan Normal University where he directs MobIle computiNg anD intelligence Sensing (MINDs) Lab. He received his B.S. degree in computer science from the University of Electronic and Science of China in 2003 and the M.S. and Ph.D. degrees in computer science from Sichuan University in 2009 and 2015, respectively. From 2015 to 2016, he was a Visiting Scholar with the University of Louisiana at Lafayette.

His current research interests include Internet of Things, Wireless Networks and Mobile Computing. He has published more than 30 peer-reviewed papers in technical conference proceedings and journals, including INFOCOM, TMC, TON, TOSN, IPDPS, TWC, TVT, etc. He has served as the Reviewer for the following journals: TMC, TOSN, Computer Networks, IEEE IoT J, and so on. He also has served as the TPC member of several conferences, such as HPCC, MSN, BigCom and EBDIT.

Charging Dynamic Sensors through Online Learning

Yu Sun, Chi Lin, Wei Yang, Jiankang Ren, Lei Wang, Guowei WU and Qiang Zhang (Dalian University of Technology, China)

As a novel solution for IoT applications, wireless rechargeable sensor networks (WRSNs) have achieved widespread deployment in recent years. Existing WRSN scheduling methods have focused extensively on maximizing the network charging utility in the fixed node case. However, when sensor nodes are deployed in dynamic environments (e.g., maritime environments) where sensors move randomly over time, existing approaches are likely to incur significant performance loss or even fail to execute normally. In this work, we focus on serving dynamic nodes whose locations vary randomly and formalize the dynamic WRSN charging utility maximization problem (termed MATA problem). Through discretizing candidate charging locations and modeling the dynamic charging process, we propose a near-optimal algorithm for maximizing charging utility. Moreover, we point out the long-short-term conflict of dynamic sensors that their location distributions in the short-term usually deviate from the long-term expectations. To tackle this issue, we further design an online learning algorithm based on the combinatorial multi-armed bandit (CMAB) model. It iteratively adjusts the charging strategy and adapts well to nodes' short-term location deviations. Extensive experiments and simulations demonstrate that the proposed scheme can effectively charge dynamic sensors and achieve a higher charging utility compared to baseline algorithms in both long-term and short-term.

Speaker Yu Sun

Yu Sun received B.E. and M.E. degrees from Dalian University of Technology, Dalian, China, in 2018 and 2020, respectively. He is studying for Ph.D. degree in School of Software Technology, Dalian University of Technology. His research interests cover wireless power transfer and wireless rechargeable sensor networks. He has authored near 10 papers in several journals and conferences including INFOCOM, IEEE/ACM ToN, ICNP, SECON, ICPP, and CN.

Session Chair

Yi Shi

Latency-First Smart Contract: Overclock the Blockchain for a while

Huayi Qi, Minghui Xu and Xiuzhen Cheng (Shandong University, China); Weifeng Lv (Beijing University of Aeronautics and Astronautics, China)

The blockchain system has a limited throughput to proceed with transactions, and sometimes gets overwhelmed by a great number of transactions. Latency-sensitive users have to bid against each other and pay more fees to make sure their transactions are processed in priority. However, the blockchain system does not keep busy all the time. In most of the time (76% in Ethereum), there is a lot of calculation power that gets wasted, during which fewer users are sending transactions. To rebalance the loads and reduce the latency for users, we propose the latency-first smart contract model that allows users to submit a commitment during the heavy-load time and then finish the rest work during their spare time. From the chain's view, the blockchain is "overclocked" shortly and then pays back. We propose a programming tool for our model and our experiment results show that applying our model reduces the latency time greatly in a heavy load.

Speaker Huayi Qi (Shandong University)

Huayi Qi received his bachelor's degree in computer science from Shandong University in 2020. He is working toward a Ph.D. degree in the School of Computer Science and Technology, Shandong University, China. His research interests include blockchain privacy and security.

On Design and Performance of Offline Finding Network

Tong Li (Renmin University of China, China); Jiaxin Liang (Huawei Technologies, China); Yukuan Ding (Hong Kong University of Science and Technology, Hong Kong); Kai Zheng (Huawei Technologies, China); Xu Zhang (Nanjing University, China); Ke Xu (Tsinghua University, China)

Recently, such industrial pioneers as Apple and Samsung have offered a new generation of offline finding network (OFN) that enables crowd search for missing devices without leaking private data. Specifically, OFN leverages nearby online finder devices to conduct neighbor discovery via Bluetooth Low Energy (BLE), so as to detect the presence of offline missing devices and report an encrypted location back to the owner via the Internet. The user
experience in OFN is closely related to the success ratio (possibility) of finding the lost device, where the latency of the prerequisite stage, i.e., neighbor discovery, matters. However, the crowd-sourced finder devices show diversity in scan modes due to different power modes or different manufacturers, resulting in local optima of neighbor discovery performance. In this paper, we present a brand-new broadcast mode called ElastiCast to deal with the scan mode diversity issues. ElastiCast captures the key features of BLE neighbor discovery and globally optimizes the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.

Speaker Tong Li (Renmin University of China)

Tong Li is currently the faculty at the Renmin University of China. His research interests include networking, distributed systems, and big data.

WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking

Jinlong E (Renmin University of China, China); Lin He and Zhenhua Li (Tsinghua University, China); Yunhao Liu (Tsinghua University & The Hong Kong University of Science and Technology, China)

With the desirable functionality of moving object tracking, wireless pan-tilt cameras are playing critical roles in a growing diversity of surveillance environments. However, today's pan-tilt cameras oftentimes underperform when tracking frequently moving objects like humans -- they are prone to lose sight of objects and bring about excessive mechanical rotations that are especially detrimental to those energy-constrained outdoor scenarios. The ineffectiveness and high cost of state-of-the-art tracking approaches are rooted in their adherence to the industry's simplicity principle, which leads to their stateless nature, performing gimbal rotations based only on the latest object detection. To address the issues, this paper presents WiseCam that wisely tunes the pan-tilt cameras to minimize mechanical rotation costs while maintaining long-term object tracking with low overhead. It is achieved by object trajectory construction in a panoramic space and online rotating angle determination based on spatio-temporal motion information, together with adaptively adjusted rotation generation and execution. We implement WiseCam on two types of pan-tilt cameras with different motors. Real-world evaluations demonstrate that WiseCam significantly outperforms the state-of-the-art tracking approaches on both tracking duration and power consumption.

Speaker Jinlong E (Renmin University of China)

He is currently a lecturer at Renmin University of China. His current research interests include cloud computing, edge computing, and IoT.

Effectively Learning Moiré QR Code Decryption from Simulated Data

Yu Lu, Hao Pan, Guangtao Xue and Yi-Chao Chen (Shanghai Jiao Tong University, China); Jinghai He (University of California, Berkeley, China); Jiadi Yu (Shanghai Jiao Tong University, China); Feitong Tan (Simon Fraser University, Canada)

Moiré QR Code is a secure encrypted QR code system that can protect the user's QR code displayed on the screen from being accessed by attackers. However, conventional decryption methods based on image processing techniques suffer from intensive computation and significant decryption latency in practical mobile applications. In this work, we propose a deep learning-based Moiré QR code decryption framework and achieve an excellent decryption performance. Considering the sensitivity of the Moiré phenomenon, collecting training data in the real world is extremely labor and material intensive. To overcome this issue, we develop a physical screen-imaging Moire simulation methodology to generate a synthetic dataset which covers the entire Moiré-visible area. Extensive experiments show that the proposed decryption network can achieve a low decryption latency (0.02 seconds) and a high decryption rate (98.8%), compared with the previous decryption method with decryption latency (5.4 seconds) and decryption rate (98.6%).

Speaker Yu Lu (Shanghai Jiao Tong University)

Yu Lu is a Ph.D. student of computer science at Shanghai Jiao Tong University. His research interests focus on networked systems and span the areas of wireless communication and sensing, human-computer interaction, and computer vision.

Session Chair

Qinghua Li

Multi-Objective Order Dispatch for Urban Crowd Sensing with For-Hire Vehicles

Jiahui Sun, Haiming Jin, Rong Ding and Guiyun Fan (Shanghai Jiao Tong University, China); Yifei Wei (Carnegie Mellon University, USA); Lu Su (Purdue University, USA)

For-hire vehicle-enabled crowd sensing (FVCS) has become a promising paradigm to conduct urban sensing tasks in recent years. FVCS platforms aim to jointly optimize both the order-serving revenue as well as sensing coverage and quality. However, such two objectives are often conflicting and need to be balanced according to the platforms' preferences on both objectives. To address this problem, we propose a novel cooperative multi-objective multi-agent reinforcement learning framework, referred to as MOVDN, to serve as the first preference-configurable order dispatch mechanism for FVCS platforms. Specifically, MOVDN adopts a decomposed network structure, which enables agents to make distributed order selection decisions, and meanwhile aligns each agent's local decision with the global objectives of the FVCS platform. Then, we propose a novel algorithm to train a single universal MOVDN that is optimized over the space of all preferences. This allows our trained model to produce the optimal policy for any preference. Furthermore, we provide the theoretical convergence guarantee and sample efficiency analysis of our algorithm. Extensive experiments on three real-world ride-hailing order datasets demonstrate that MOVDN outperforms strong baselines and can support the platform in decision-making effectively.

Speaker Haiming Jin (Shanghai Jiao Tong University)

I am currently a tenure-track Associate Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU). From August 2021 to December 2022, I was a tenure-track Associate Professor in the John Hopcroft Center (JHC) for Computer Science at SJTU. From September 2018 to August 2021, I was an assistant professor in JHC at SJTU. From June 2017 to June 2018, I was a Postdoctoral Research Associate in the Coordinated Science Laboratory (CSL) of University of Illinois at Urbana-Champaign (UIUC). I received my PhD degree from the Department of Computer Science of UIUC in May 2017, advised by Prof. Klara Nahrstedt. Before that, I received my Bachelor degree from the Department of Electronic Engineering of SJTU in July 2012.

AoI-aware Incentive Mechanism for Mobile Crowdsensing using Stackelberg Game

Mingjun Xiao, Yin Xu and Jinrui Zhou (University of Science and Technology of China, China); Jie Wu (Temple University, USA); Sheng Zhang (Nanjing University, China); Jun Zheng (University of Science and Technology of China, China)

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the data freshness. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism, whereby the platform and workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI.

Speaker Yin Xu

Yin Xu received her B.S. degree from the School of Computer Science and Technology at the Anhui University (AHU), Hefei, China, in 2019. She is currently a PhD student in the School of Computer Science and Technology at the University of Science and Technology of China (USTC), Hefei, China. Her research interests include mobile crowdsensing, federated learning, privacy preservation, game theory, edge computing, and incentive mechanism design.

Crowd²: Multi-agent Bandit-based Dispatch for Video Analytics upon Crowdsourcing

Yu Chen, Sheng Zhang, Yuting Yan, Yibo Jin, Ning Chen and Mingtao Ji (Nanjing University, China); Mingjun Xiao (University of Science and Technology of China, China)

Many crowdsourcing platforms are emerging, leveraging the resources of recruited workers to execute various outsourcing tasks, mainly for those computing-intensive video analytics with high quality requirements. Although the profit of each platform is strongly related to the quality of analytics feedback, due to the uncertainty on diverse performance of workers and the conflicts of interest over platforms, it is non-trivial to determine the dispatch of tasks with maximum benefits. In this paper, we design a decentralized mechanism for a Crowd of Crowdsourcing platforms, denoted as Crowd2, optimizing the worker selection to maximize the social welfare of these platforms in a long-term scope, under the consideration of both proportional fairness and dynamic flexibility. Concretely, we propose a video analytics dispatch algorithm based on multi-agent bandit, for which the more accurate profit estimates are attained via the decoupling of multi-knapsack based mapping problem. Via rigorous proofs, a sub-linear regret bound for social welfare of crowdsourcing profits is achieved while both fairness and flexibility are ensured. Extensive trace-driven experiments demonstrate that Crowd2 improves the social welfare by 36.8%, compared with other alternatives.

Speaker Yu Chen (Nanjing University)

Yu Chen received the BS degree from the Department of Computer Science and Technology, Nanjing University, China, in 2019, where he is currently working toward the PhD degree under the supervision of associate professor Sheng Zhang. He is a member of the State Key Laboratory for Novel Software Technology. To date, he has published more than 10 papers, in journals such as TPDS and Journal of Software, and conferences such as INFOCOM, ICPP, IWQoS, ICC and ICPADS. His research interests include video analytics and edge computing.

Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing

En Wang, Weiting Liu and Wenbin Liu (Jilin University, China); Chaocan Xiang (Chongqing University, China); Bo Yang and Yongjian Yang (Jilin University, China)

Mobile CrowdSensing (MCS) is a data sensing model that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has become a practical paradigm for large-scale and fine-grained urban sensing with the advantage of collecting only a few data to infer the full map. However, in many real-world scenarios, such as early prevention of epidemic, users are interested not only in inferring the entire sensing map, but also in long-term prediction the sensing map, where the latter is more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a novel model of Spatiotemporal Transformer (ST-transformer) to infer and predict the data with the sparse sensed data based on the spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data.

Speaker Weiting Liu (Jilin University)

Weiting Liu received his bachelor’s degree in software engineering from Jilin University, Changchun, China, in 2020. Currently, he is studying for the master’s degree in computer science and technology from Jilin University, Changchun, China. His current research focuses on mobile crowdsensing, spatiotemporal data processing.

Session Chair

Qinghua Li

Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing

Tian Dong (Shanghai Jiao Tong University, China); Ziyuan Zhang (Beijing University of Posts and Telecommunications, China); Han Qiu (Tsinghua University, China); Tianwei Zhang (Nanyang Technological University, Singapore); Hewu Li (Tsinghua University, China); Terry Wang (Alibaba, China)

Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit architectures can achieve inference and transmission efficiency by fragmenting and distributing a large DNN model in edge computing scenarios (e.g. edge devices and cloud servers). In this paper, we propose a novel backdoor attack specifically on the dynamic multi-exit DNN models. Particularly, we inject a backdoor by poisoning one DNN model's shallow hidden layers targeting not this vanilla DNN model but only at its dynamically deployed multi-exit architectures. Our backdoored vanilla model behaves normally on performance and cannot be activated even with the correct trigger. However, the backdoor will be activated when the victims acquire this model and transform it into a dynamic multi-exit architecture at their deployment. We conduct extensive experiments to prove the effectiveness of our attack on three structures (ResNet-56, VGG-16, and MobileNet) with four datasets (CIFAR-10, SVHN, GTSRB, and Tiny-ImageNet) and our backdoor is stealthy to evade multiple state-of-the-art backdoor detection or removal methods.

Speaker Ziyuan Zhang (Beijing University of Posts and Telecommunications)

Ziyuan Zhang is a senior student from Beijing University of Posts and Telecommunications. Her current research focuses on edge computing security issues.

A Comprehensive and Long-term Evaluation of Tor V3 Onion Services

Chunmian Wang, Luo Junzhou and Zhen Ling (Southeast University, China); Lan Luo (University of Central Florida, USA); Xinwen Fu (University of Massachusetts Lowell, USA)

To enhance the privacy of Tor onion services, the new generation onion service protocol, i.e., version 3 (V3), is deployed to deeply hide the domain names of onion services. However, existing onion service analysis methods cannot be used any more to understand V3 onion services. We address the issue in this paper. To understand the scale of V3 onion services, we theoretically analyze the V3 onion service mechanism and propose an accurate onion service size estimation method, which is able to achieve an estimation deviation of 2.43\% on a large-scale Tor emulation network. To understand onion website popularity, we build a system and collect more than two years of data of public onion websites. We develop an onion service popularity estimation algorithm using online rate and access rate to rank the onion services. To reduce the noise from the phishing websites, we cluster onion websites into groups based on the content and structure. To our surprise, we only find 487 core websites out of the collected 45,889 public onion websites. We further analyze the weighted popularity using yellow page data within each group and discover that 35,331 phishing onion websites spoof the 487 core websites.

Speaker Chunmian Wang(Southeast University, China)

DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks

Jie Wen (East China Jiaotong University, China & University of South China, China); Nan Jiang (East China Jiaotong University, China); Jin Li (Guangzhou University, China); Ximeng Liu (Fuzhou University, China); Honglong Chen (China University of Petroleum, China); Yanzhi Ren (University of Electronic Science and Technology of China, China); Zhaohui Yuan and Ziang Tu (East China Jiaotong University, China)

The social trust assessment can spur extensive applications but remain a challenging problem having limited exploration. Such explorations mainly limit their studies to the static network topology or simplified dynamic network, toward the social trust relationship prediction. We explore the social trust by taking into account time-varying online social networks(OSNs) whereas the social trust relationship may vary over time. The DTrust, a dynamic graph attention-based solution, will be proposed for accurate social trust prediction. DTrust is composed of a static aggregation unit and a dynamic unit, respectively responsible for capturing both the spatial dependence features and temporal dependence features. In the former unit, we stack multiple NNConv layers derived from the edge-conditioned convolution network for capturing the spatial dependence features correlated to the network topology and the observed social relationships. In the latter unit, a gated recurrent unit (GRU) will be employed for learning the evolution law of social interaction and social trust relationships. Based on the extracted spatial and temporal features, we then employ the neural networks for learning, able to predict the social trust relationships for both current and future time slots. Extensive experimental results exhibit that our DTrust can outperform the benchmark counterparts on two real-world datasets.

Speaker Jie Wen(East China Jiaotong University)

Jie Wen was born in 1983. He received the M.Sc. degree (2008) in Control Science and Engineering

from Central South University. He is currently pursuing the Ph.D. degree in control systems at East China Jiaotong University. His current research interests focus on Industrial Internet of Things, Age of Information, Graph Neural Network and Social Network.

SDN Application Backdoor: Disrupting the Service via Poisoning the Topology

Shuhua Deng, Xian Qing and Xiaofan Li (Xiangtan University, China); Xing Gao (University of Delaware, USA); Xieping Gao (Hunan Normal University, China)

Software-Defined Networking (SDN) enables the deployment of diversified networking applications by providing global visibility and open programmability on a centralized controller. As SDN enters its second decade, several well-developed open source controllers have been widely adopted in industry, and various commercial SDN applications are built to meet the surging demand of network innovation. This complex ecosystem inevitably introduces new security threats, as malicious applications can significantly disrupt network operations. In this paper, we introduce a new vulnerability in existing SDN controllers that enable adversaries to create a backdoor and further deploy malicious applications to disrupt network service via a series of topology poisoning attacks. The root cause of this vulnerability is that SDN systems simply process received Packet-In messages without checking the integrity, and thus can be mis-guided by manipulated messages. We discover that five popular SDN controllers (i.e., Floodlight, ONOS, OpenDaylight, POX and Ryu) are potentially vulnerable to the disclosed attack, and further propose six new attacks exploiting this vulnerability to disrupt SDN services from different layers. We evaluate the effectiveness of these attacks with experiments in real SDN testbeds, and discuss feasible countermeasures.

Speaker Xian Qing (Xiangtan University)

Xian Qing is a postgraduate student from Xiangtan University. Her current research focuses on software-defined networking.

Session Chair

Yang Xiao

Program at a Glance